System and method for using first-principles simulation to characterize a semiconductor manufacturing process

ABSTRACT

A method, system and computer readable medium for facilitating a process performed by a semiconductor processing tool. The method includes inputting data relating to a process performed by the semiconductor processing tool, and inputting a first principles physical model relating to the semiconductor processing tool. First principles simulation is then performed using the input data and the physical model to provide a simulation result for the process performed by the semiconductor processing tool, and the simulation result is used as part of a data set that characterizes the process performed by the semiconductor processing tool.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to manufacturing semiconductordevices, and more specifically to use of first principles simulation insemiconductor manufacturing processes.

2. Discussion of the Background

Material processing in the semiconductor industry presents formidablechallenges in the manufacture of integrated circuits (ICs). Demands forincreasing the speed of ICs in general, and memory devices inparticular, force semiconductor manufacturers to make devices smallerand smaller on the substrate surface. Moreover, in order to reducefabrication costs, it is necessary to reduce the number of steps (e.g.,etch steps, deposition steps, etc.) required to produce an IC structureand hence reduce the overall complexity of the IC structure and thefabrication methods thereof. These demands are further exacerbated byboth the reduction in feature size and the increase of substrate size(i.e., 200 mm to 300 mm and greater) which places greater emphasis onthe precise control of critical dimensions (CD), process rate, andprocess uniformity to maximize the yield of superior devices.

In semiconductor manufacturing, numerous steps during the evolution ofICs are employed including vacuum processing, thermal processing, plasmaprocessing, etc. Within each processing step, numerous variables arepresent that affect the outcome of the process. In order to moreprecisely control the outcome of each processing step, the respectiveprocess tools are equipped with an increasing number of diagnosticsystems (electrical, mechanical, and optical) to measure data duringprocessing and provide an intelligent basis for correcting processvariations through the actions of a process controller. The number ofdiagnostic systems is becoming burdensome and costly. Yet, datasufficiently resolved in space and time for complete process control isstill not available.

These industry and manufacturing challenges have led to interest in moreuse of computer based modeling and simulation in the semiconductormanufacturing industry. Computer-based modeling and simulation areincreasingly being used for prediction of tool performance during thesemiconductor manufacturing tool design process. The use of modelingallows the reduction of both cost and time involved in the tooldevelopment cycle. Modeling in many disciplines, such as stress,thermal, magnetics, etc., has reached a level of maturity where it canbe trusted to provide accurate answers to design questions. Moreover,computer power has been increasing rapidly along with the development ofnew solution algorithms, both of which resulted in reduction of timerequired to obtain a simulation result. Indeed, the present inventorshave recognized that a large number of simulations typically done in thetool design stage can presently be run in times comparable to wafer orwafer cassette processing times. These trends have led to the suggestionthat simulation capability typically used only for tool design can beimplemented directly on the tool itself to aid in various processesperformed by the tool. For example, the 2001 International TechnologyRoadmap for Semiconductors identifies issues impeding the development ofon-tool integrated simulation capability as an enabling technology formanufacturing very small features in future semiconductor devices.

Indeed, the failure of industry to implement on-tool simulation tofacilitate tool processes is primarily due to the need for computationalresources capable of performing the simulations in a reasonable time.Specifically, the processor capabilities currently dedicated tosemiconductor manufacturing tools are typically limited to diagnosticand control functions, and therefore could only perform relativelysimple simulations. Thus, the semiconductor manufacturing industry hasperceived a need to provide powerful dedicated computers in order torealize meaningful on-tool simulation capabilities. However, dedicationof such a computer to the semiconductor processing tool results inwasted computational resources when the tool runs processes that usesimple simulations, or no simulations at all. This inefficient use of anexpensive computational resource has been a major impediment toimplementation of simulation capabilities on semiconductor processingtools.

SUMMARY OF THE INVENTION

One object of the present invention is to reduce or solve the aboveidentified and/or other problems of the prior art.

Another object of the present invention is to integrate first principlessimulations capabilities with a semiconductor manufacturing tool inorder to facilitate a process performed by the tool.

Yet another object of the present invention is to provide toolsimulation capabilities without the need for powerful computationalresources dedicated to the tool.

Still another object of the present invention is to provide broad basedon-tool simulation capabilities using existing computational resourcesdedicated to each tool in a manufacturing facility.

These and/or other objectives may be provided by the following aspectsof the invention:

On aspect of the present invention is a method of facilitating a processperformed by a semiconductor processing tool, which includes inputtingdata relating to a process performed by the semiconductor processingtool, and inputting a first principles physical model relating to thesemiconductor processing tool. First principles simulation is thenperformed using the input data and the physical model to provide asimulation result for the process performed by the semiconductorprocessing tool, and the simulation result is used as part of a data setthat characterizes the process performed by the semiconductor processingtool.

Another aspect of the invention is a system that includes asemiconductor processing tool configured to perform a process, and aninput device configured to input data relating to the process performedby the semiconductor processing tool. A first principles simulationprocessor is configured to input a first principles physical modelrelating to the semiconductor processing tool, and perform firstprinciples simulation using the input data and the physical model toprovide a first principles simulation result for the process performedby the semiconductor processing tool. The simulation result is used aspart of a data set that characterizes the process performed by thesemiconductor processing tool.

Yet another aspect of the invention is a system for facilitating aprocess performed by a semiconductor processing tool, the systemincluding means for inputting data relating to a process performed bythe semiconductor processing tool, and means for inputting a firstprinciples physical model relating to the semiconductor processing tool.The system also includes means for performing first principlessimulation using the input data and the physical model to provide asimulation result for the process performed by the semiconductorprocessing tool, and means for using the simulation result as part of adata set that characterizes the process performed by the semiconductorprocessing tool.

Still another aspect of the invention is a computer readable medium thatcontains program instructions for execution on a processor, which whenexecuted by the computer system, cause the processor to perform thesteps of inputting data relating to a process performed by thesemiconductor processing tool, and inputting a first principles physicalmodel relating to the semiconductor processing tool. The processor isalso caused to perform first principles simulation using the input dataand the physical model to provide a simulation result for the processperformed by the semiconductor processing tool, and use the simulationresult as part of a data set that characterizes the process performed bythe semiconductor processing tool.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the invention and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a block diagram of a system for using first principlessimulation techniques to facilitate a process performed by asemiconductor processing tool in accordance with an embodiment of thepresent invention;

FIG. 2 is a flow chart showing a process for using first principlessimulation techniques to facilitate a process performed by asemiconductor processing tool in-accordance with an embodiment of thepresent invention;

FIG. 3 is a block diagram of a network architecture that may be used toprovide first principles simulation techniques to facilitate a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention;

FIG. 4 is a block diagram of a system for using first principlessimulation techniques to provide virtual sensor measurements on asemiconductor processing tool in accordance with an embodiment of thepresent invention;

FIG. 5 is a block diagram of a system for using first principlessimulation techniques to characterize a process on a semiconductorprocessing tool in accordance with an embodiment of the presentinvention;

FIG. 6 is a block diagram of a system for using first principlessimulation techniques to control a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention;

FIG. 7 is a flow chart showing a process for using first principlessimulation techniques to control a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention;

FIG. 8 is a block diagram of a system for using first principlessimulation techniques and an empirical model to control a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention;

FIG. 9 is a flow chart showing a process for using first principlessimulation techniques and an empirical model to control a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention;

FIG. 10 is a block diagram of a system for using first principlessimulation techniques and a fault detector to control a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention;

FIG. 11 is a schematic representation of the data inputs, X and Y, to aPLS analysis and the corresponding outputs T, P, Ū, C, W, Ē, F, H andvariable importance in the projection (VIP);

FIG. 12 is a flow chart showing a process for using first principlessimulation techniques to detect a fault and control a process performedby a semiconductor processing tool in accordance with an embodiment ofthe present invention;

FIG. 13 is a block diagram of a vacuum processing system, to which aprocess control embodiment of the present invention may be applied; and

FIG. 14 illustrates a computer system upon which an embodiment of thepresent invention may be implemented.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, FIG. 1 isa block diagram of a system for using first principles simulationtechniques to facilitate a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention. As seen in FIG. 1, the system includes a semiconductorprocessing tool 102, a data input device 104, a first principlesphysical model 106, and a first principles simulation processor 108. Thesystem of FIG. 1 may also include a tool level library 110 as shown inphantom.

Semiconductor processing tool 102 is a tool for performing a processrelated to manufacturing an integrated circuit or semiconductor wafer.For example, the semiconductor processing tool 102 may be implemented asa material processing system, an etch system, a photoresist spin coatingsystem, a lithography system, a dielectric coating system (i.e. aspin-on-glass (SOG) or spin-on-dielectric (SOD) system), a depositionsystem (i.e. a chemical vapor deposition (CVD) system or a physicalvapor deposition (PVD) system), a rapid thermal processing (RTP) systemfor thermal annealing, a batch diffusion furnace, or any other tool forperforming a semiconductor manufacturing process.

Data input device 104 is a device for collecting data relating to aprocess performed by the semiconductor processing tool 102 and inputtingthe collected data to the first principles simulation processor 106. Theprocess performed by the semiconductor process tool 102 may be acharacterization process (i.e. process design or development), acleaning process, a production process, or any other process performedby the semiconductor processing tool. In one embodiment, the data inputdevice 104 may be implemented as a physical sensor for collecting dataabout the semiconductor processing tool 102 itself, and/or theenvironment contained within a chamber of the tool. Such data mayinclude fluid mechanic data such as gas velocities and pressures atvarious locations within the process chamber, electrical data such asvoltage, current, and impedance at various locations within theelectrical system of the process chamber, chemical data such as specieconcentrations and reaction chemistries at various locations within theprocess chamber, thermal data such as gas temperature, surfacetemperature, and surface heat flux at various locations within theprocess chamber, plasma processing data (when plasma is utilized) suchas a plasma density (obtained, for example, from a Langmuir probe), anion energy (obtained, for example, from an ion energy spectrumanalyzer), and mechanical data such as pressure, deflection, stress, andstrain at various locations within the process chamber.

In addition to the tool and tool environment data, the data input device104 may collect data relating to the process itself, or process resultsobtained on a semiconductor wafer that the tool 102 is performing aprocess on. In one embodiment, data input device 104 is implemented as ametrology tool coupled to the semiconductor processing tool 102. Themetrology tool may be configured to measure process performanceparameters such as: etch rate, deposition rate, etch selectivity (ratioof the rate at which a first material is etched to the rate at which asecond material is etched), an etch critical dimension (e.g. length orwidth of feature), an etch feature anisotropy (e.g. etch featuresidewall profile), a film property (e.g. film stress, porosity, etc.), amask (e.g. photoresist) film thickness, a mask (e.g. photoresist)pattern critical dimension, or any other parameter of a processperformed by the semiconductor processing tool 102.

The data input device may be directly coupled to the process tool 102and first principles simulation processor 106 to automatically receivedata from the tool 102 and forward this data to the first principlessimulation processor 106, as shown in FIG. 1. Alternatively, the datainput device 104 may be implemented as a user input device used toindirectly provide data relating to a process performed by thesemiconductor processing tool 102 to the simulation processor 106. Forexample, data input device 104 may be a keyboard that a simulationoperator uses to input data into the first principles simulationprocessor 106. Still alternatively, the data input device may be adatabase for storing data relating to processes performed in the past bythe semiconductor processing tool 102. In this embodiment, the databasemay be populated automatically by use of a physical sensor or metrologytool coupled to the semiconductor processing tool 102, and/or by manualinput. The database may be automatically accessed by the firstprinciples simulation processor 108 to input the data to the processor.

First principles physical model 106 is a model of the physicalattributes of the tool and tool environment as well as the fundamentalequations necessary to perform first principles simulation and provide asimulation result for facilitating a process performed by thesemiconductor processing tool. Thus, the first principles physical model106 depends to some extent on the type of semiconductor processing tool102 analyzed as well as the process performed in the tool. For example,the physical model 106 may include a spatially resolved model of thephysical geometry of the tool 102, which is different, for example, fora chemical vapor deposition (CVD) chamber and a diffusion furnace.Similarly, the first principles equations necessary to compute flowfields are quite different than those necessary to compute temperaturefields. The physical model 106 may be a model as implemented incommercially available software, such as ANSYS, of ANSYS Inc.,Southpointe, 275 Technology Drive Canonsburg, Pa. 15317, FLUENT, ofFluent Inc., 10 Cavendish Ct. Centerra Park, Lebanon, N.H. 03766, orCFD-ACE+, of CFD Research Corp., 215 Wynn Dr., Huntsville, Ala. 35805,to compute flow fields, electromagnetic fields, temperature fields,chemistry, surface chemistry (i.e. etch surface chemistry or depositionsurface chemistry). However, special purpose or custom models developedfrom first principles to resolve these and other details within theprocessing system may also be used.

First principles simulation processor 108 is a processing device thatapplies data input from the data input device 104 to the firstprinciples physical model 108 to execute a first principles simulation.Specifically, the first principles simulation processor 108 may use thedata provided by the data input device 104 to set initial conditionsand/or boundary conditions for the first principles physical model 106,which is then executed by the simulation module. First principlessimulations in the present invention include, but are not limited to,simulations of electromagnetic fields derived from Maxwell's equations,continuum simulations, for example, for mass, momentum, and energytransport derived from continuity, the Navier-Stokes equation and theFirst Law of Thermodynamics, as well as atomistic simulations derivedfrom the Boltzmann equation, such as for example Monte Carlo simulationsof rarefied gases (see Bird, G. A. 1994. Molecular gas dynamics and thedirect simulation of gas flows, Clarendon Press). First principlessimulation processor 108 may be implemented as a processor orworkstation physically integrated with the semiconductor processing tool102, or as a general purpose computer system such as the computer system1401 of FIG. 14. The output of the first principles simulation processor108 is a simulation result that is used to facilitate a processperformed by the semiconductor processing tool 102. For example, thesimulation result may be used to facilitate process development, processcontrol and fault detection as well as to provide virtual sensor outputsthat facilitate tool processes, as will be further described below.

As shown in phantom in FIG. 1, the system may also include a tool-levellibrary 108 for storage of simulation results. The library isessentially a compilation of results of past simulations that may beused to provide simulation results in the future. The tool level library110 may be stored in a separate storage device or in a computer storagedevice, such as a hard disk, integrated with the first principlessimulation processor 106.

It is to be understood that the system in FIG. 1 is for exemplarypurposes only, as many variations of the specific hardware and softwareused to implement the present invention will be readily apparent to onehaving ordinary skill in the art. For example, the functionality of thefirst principles physical model 106, first principles simulationprocessor 108, and tool level library 110 may be combined in a singledevice. Similarly, the functionality of the data input device 104 may becombined with the functionality of the semiconductor processing tool 102and/or the first principles simulation processor 108. To implement thesevariations as well as other variations, a single computer (e.g., thecomputer system 1401 of FIG. 14) may be programmed to perform thespecial purpose functions of two or more of the devices shown in FIG. 1.On the other hand, two or more programmed computers may be substitutedfor one of the devices shown in FIG. 1. Principles and advantages ofdistributed processing, such as redundancy and replication, may also beimplemented as desired to increase the robustness and performance of thesystem, for example.

FIG. 2 is a flow chart showing a process for using first principlessimulation techniques to facilitate a process performed by asemiconductor processing tool in accordance with an embodiment of thepresent invention. The process shown in FIG. 2 may be run on the firstprinciples simulation processor 104 of FIG. 1, for example. As seen inFIG. 2, the process begins in step 201 with the inputting of datarelated to a process performed by the semiconductor processing tool 102.As discussed above, the input data may be data relating to physicalattributes of the tool/tool environment and/or data relating to aprocess performed by the tool on a semiconductor wafer or results ofsuch process. As also described above, the input data may be directlyinput from a physical sensor or metrology tool coupled to the firstprinciples simulation processor 104, or indirectly input from a manualinput device or database. Where the data is indirectly input by manualinput device or a database, the data may be data that was recorded froma previously run process, such as sensor data from a previously runprocess. Alternatively the data may be set by the simulation operator as“best known input parameters” for the particular simulations, which mayor may not relate to the data collected during a process. The type ofinput data input by the processing tool generally depends on the desiredsimulation result.

In addition to inputting the input data, the first principles simulationprocessor 104 also inputs the first principles physical model 106 asshown by step 203. Step 203 includes inputting the physical attributesof the tool modeled by the model, as well as first principles equationscodified in software necessary to perform a first principles simulationof a desired attribute of the process performed by the semiconductorprocessing tool 102. The first principles physical model 106 may beinput to the processor from an external memory or an internal memorydevice integral to the processor. Moreover, while step 203 is shown inFIG. 2 as following step 201, it is to be understood that the firstprinciples simulation processor 104 may perform these stepssimultaneously or in reverse of the order shown in FIG. 2.

In step 205, the first principles simulation processor 108 uses theinput data of step 201 and the first principles physical model of step203 to execute a first principles simulation and provide a simulationresult. Step 205 may be performed either concurrently with or notconcurrently with the process performed by the semiconductor processingtool. For example, simulations that can be performed at short solutiontimes may be run concurrently with a tool process, and results used tocontrol the process. More computationally intensive simulations may beperformed not concurrently with the tool process and the simulationresult may be stored in a library for later retrieval. In oneembodiment, step 205 includes using the input data of step 201 to setinitial and/or boundary conditions for the physical model provided instep 205.

Once the simulation is executed, the simulation result is used tofacilitate a process performed by the semiconductor processing tool 102.As used herein, the term “facilitate a process performed by thesemiconductor processing tool” includes using the simulation result forexample to detect a fault in the process, to control the process, tocharacterize the process for manufacturing runs, to provide virtualsensor readings relating to the process, or any other use of thesimulation result in conjunction with facilitating a process performedby the semiconductor processing tool 102.

FIG. 3 is a block diagram of a network architecture that may be used toprovide first principles simulation techniques to facilitate a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention. As seen in this figure, the networkarchitecture includes a device manufacturing fab connected to remoteresources via the Internet 314. The device manufacturing fab includes aplurality of semiconductor processing tools 102 connected to respectivesimulation modules 302. As described with respect to FIG. 1, eachsemiconductor processing tool 102 is a tool for performing a processrelated to manufacturing a semiconductor device such as an integratedcircuit. Each simulation module 302 is a computer, workstation, or otherprocessing device capable of executing first principles simulationtechniques to facilitate a process performed by a semiconductorprocessing tool 102. Thus, each simulation module 302 includes the firstprinciples physical model 106 and the first principles simulationprocessor 108 described with respect to FIG. 1, as well as any otherhardware and/or software that may be helpful for executing firstprinciples simulations. Moreover, simulation modules 302 are configuredto communicate with the fab-level advanced process control (APC)controller using any known network communication protocol. Eachsimulation module 302 may be implemented as a general purpose computersuch as the computer system 1401 of FIG. 14.

While not shown in FIG. 3, each simulation module 302 is associated witha data input device for inputting data relating to a process performedby a tool 102. In the embodiment of FIG. 3, the simulation modules 302are directly coupled to a respective tool 102, and therefore, the datainput device is implemented as a physical sensor and/or metrology toolphysically mounted on a respective tool 102. However, as noted above,the data input device may be implemented as a manual input device usedby the simulation module operator, or a database. In addition, eachsimulation module 302 may be configured to store information in andretrieve information from a tool-level library such as the library 306.As also noted above, the tool level library is essentially a compilationof past simulation results that may be useful for simulations in thefuture.

In one embodiment of the present invention, each simulation module 302is connected to main fab-level APC controller 304 via networkconnections. As seen in FIG. 3, the fab-level APC controller 304 mayalso be connected to a standalone simulation module 308 and fab-levellibrary 310 as well as the standalone simulation module 312 via Internet314 and communications server 316.

The standalone simulation modules 308 and 312 are computationalresources that may be used to aid the simulation modules 302 inperforming computationally intensive first principles simulations aswill be further described below. The fab-level library 310 is a databasefor storing simulation results obtained from any of the simulationmodules of the network system. The fab-level APC controller 304 is anysuitable workstation, server, or other device for communicating with thesimulation modules 302, 308 and 312, and for storing information in andretrieving information from the fab-level library 310. The fab-level APCcontroller 304 also facilitates processes performed by the tools 102based on simulation results of the simulation modules 302. For example,the APC controller may be configured to receive a simulation result froma simulation module and use the simulation result to implement a controlmethodology for process adjustment and/or correction of any of the tools102. The fab-level APC controller 304 communicates with the simulationmodules 302, 308 and 312, and the fab-level library 310 using anysuitable protocol and may be implemented using the computer system 1401of FIG. 14, for example.

The present inventors have discovered that the network configuration ofFIG. 3 provides computational and storage resource sharing that allows abroad range of first principles simulation results at reasonablesolution speeds, thus providing meaningful on-tool simulationcapabilities that can facilitate processes performed by the tool.Specifically, while simple simulations may be executed by a tool'sdedicated simulation module, complex simulations requiring greatercomputational resources may be executed using code parallelizationtechniques on multiple simulation modules in the network that may beon-tool or standalone. Even on-tool simulation modules in equipmentcurrently under preventive maintenance may be used as a sharedcomputational resource, provided there is power to the simulationmodule. Similarly, simulation results used for later lookup can bestored in libraries (e.g. storage devices) anywhere in the fab network,and accessed by all tools when lookups of diagnostic or control data aremade.

The present inventors have also discovered that the network architectureof FIG. 3 provides the ability to distribute model results done at oneprocessing tool 102 for one condition set, to other similar or identicaltools operating later under the same or similar conditions, so redundantsimulations are eliminated. Running simulations only for uniqueprocessing conditions at on-tool and standalone modules and re-usingresults from similar tools that have already known simulated solutionsallows for rapid development of lookup libraries containing results thatcan be used for diagnostics and control over a large range of processingconditions. Further, the reuse of the known solutions as initialconditions for first principles simulation reduces the computationalrequirements and facilitates the production of simulated solutions in atime frame consistent with on-line control. Similarly, the networkarchitecture of FIG. 3 also provides the ability to propagate changesand refinements made to physical models and model input parameters fromone simulation module to others in the network. For example, if duringprocess runs and parallel executions of a model it is determined thatsome input parameters need to be changed, then these changes can bepropagated to all other simulation modules and tools via the network.

The network architecture of FIG. 3 also permits an optional connectionto remote computational resources including simulation modules that canassist in executing simulation tasks and communicating the results backto the device maker fab. Connection to remote resources can be made byway of a secure connection, such as a Virtual Private Network. Suchsecure connections may also be established to third-parties that providecomputational resources to support first-principles simulation onprocessing tools. Similarly the remote communications server can act asa “clearinghouse” for most up-to-date software, models, inputparameters, and simulation results, which multiple customers can use,thereby further increasing the speed at which accurate results librariesare created. These updated models can be uploaded from a customer siteto the remote resources, analyzed, and if it is determined that therefinement applies to most customers, the refinement is made availablevia the communications server and Internet connection to othercustomers.

Thus, the present inventors have discovered meaningful on-toolsimulation capabilities that can facilitate processes performed by thetool without the need for expensive computers dedicated to the tool.Based on this discovery, the present inventors further developed novelon-tool simulation systems for providing virtual sensor readings,providing characterization data for use in developing processesperformed by the tool, and for providing process fault detection andprocess control capabilities. These uses of the inventive on-toolsimulation to facilitate a process performed by the semiconductorprocessing tool may be implemented on a single tool and simulationmodule, or on an interconnected network of computational and storageresources such as that described in FIG. 3.

Specifically, on-tool simulation results may be used to augment measureddata sets from physical sensors. One of the shortcomings ofcurrent-generation semiconductor processing tools is the relativelysmall number of sensors being used to characterize the currently runningprocess, particularly on production tools. Installing more sensors on atool becomes a very expensive proposition if the number of requiredsensors is large, and in many cases, there is no space left on the toolfor modification and installation of additional sensors. Yet, even inproduction tools, there are situations in which “measurements” areneeded in locations where sensors cannot be installed. The on-tool firstprinciples simulation capability of the present invention provides therequired “measurements” without any additional hardware, provided robustmodels exist to predict the measurements using other actual measurementsas initial and/or boundary conditions. In this document, the term“virtual sensor” is used to refer to a “sensor” in which themeasurements are actually provided by predictions from an on-toolsimulation.

FIG. 4 is a flow chart showing a process for using first principlessimulation techniques to provide virtual sensor readings that mayfacilitate a process performed by a semiconductor processing tool inaccordance with an embodiment of the present invention. The processshown in FIG. 4 may be run on the first principles simulation processor108 of FIG. 1, for example, or using the network architecture of FIG. 3.As seen in FIG. 4, the process begins in step 401 with inputting datafor obtaining a virtual sensor reading relating to a process performedby the semiconductor processing tool 102. The data input in step 401 maybe any of the data types described with respect to step 201 of FIG. 2,as long as the input data enables a first principles simulation toprovide a virtual sensor simulation result. Thus, the input data may bedata related to physical attributes of the tool/tool environment, aprocess performed by the tool on a semiconductor wafer, or the resultsof such process. Moreover, the input data of step 401 may be directlyinput from a physical sensor or metrology tool coupled to the firstprinciples simulation processor 108, or indirectly input from a manualinput device or database.

In one example of using metrology data as input data for obtaining avirtual sensor reading, metrology data pertaining to an etch maskpattern and underlying film thickness can serve as input to a firstprinciples etch process model, and subsequently performed etch process.Prior to performing the etch process, measurements of the mask patternincluding pattern critical dimension(s) and mask film thickness at oneor more locations (e.g., center and edge) on a given substrate for agiven substrate lot can be provided as input to the etch process model.Moreover, measurements of the underlying film thickness (i.e., filmthickness of the film to be etched) can also serve as input to the etchprocess model. Following execution of the first principles etch processmodel for a specified process recipe, and the above identified metrologyinput data, the time for completing the etch process at, for example,the center and edge can be calculated as output, and this output can beutilized to determine an over-etch period and any process adjustmentnecessary to preserve, for example, feature critical dimensionscenter-to-edge. Thereafter, these results can be utilized to adjust theprocess recipe for the current or upcoming substrate lot.

Where the data is indirectly input by manual input device or a database,the data may be data that was recorded from a previously run process,such as sensor data from a previously run process. Alternatively thedata may be set by the simulation operator as “best known inputparameters” for the particular simulations, which may or may not relateto the data collected during a process. The type of input data input bythe processing tool generally depends on the desired virtual sensormeasurements to be obtained.

In addition to inputting the input data, the first principles simulationprocessor 108 also inputs the first principles physical model foremulating a physical sensor as shown by step 403. Step 403 includesinputting the physical attributes of the tool modeled by the model, aswell as the first principles fundamental equations necessary to performa first principles simulation to obtain a virtual sensor reading thatcan substitute for a physical sensor reading relating to the processperformed by the semiconductor processing tool 102. The first principlesphysical model of step 403 may be input to the processor from anexternal memory or an internal memory device integral to the processor.Moreover, while step 403 is shown in FIG. 4 as following step 401, it isto be understood that the first principles simulation processor 104 mayperform these steps simultaneously or in reverse of the order shown inFIG. 4.

In step 405, a first principles simulation processor, such as theprocessor 108 of FIG. 1, uses the input data of step 401 and the firstprinciples physical model of step 403 to execute a first principlessimulation and provide a virtual sensor measurement. Step 405 may beperformed either at a different time, or concurrently with the processperformed by the semiconductor processing tool. Simulations run notconcurrently with the wafer process may use initial and boundaryconditions stored from previous process runs with the same or similarprocess conditions. As noted with respect to FIG. 2 above, this issuitable in cases when the simulation runs slower than the waferprocess; time may be used between wafer cassettes and even during toolshutdowns for preventive maintenance, for example, to have thesimulation module solve for required measurements. These “measurements”can later be displayed during the wafer process as if they were solvedfor concurrently with the wafer process, and if the process is executedunder the same process conditions as the simulation was run.

Where the first principles simulation is run concurrently with theprocess performed by the semiconductor tool, the data input in step 401may be data from physical sensors mounted on the semiconductorprocessing tool to sense a predetermined parameter during the processrun by the tool. In this embodiment, steady-state simulations arerepeatedly run concurrently with the process by using the physicalsensor measurements to repeatedly update boundary conditions of thefirst principles simulation model. The virtual measurement datagenerated is useful for monitoring by tool operators, and in no waydiffers from measurements made by physical sensors. However, thesimulation is preferably capable of running fast so virtual measurementscan be updated at a reasonable rate (e.g. “sampling rate”). The firstprinciples simulation may also be run concurrently without the use ofphysical sensor input data. In this embodiment, initial and boundaryconditions for the simulation are set based on the initial setting ofthe tool prior to a tool process and the readings of physical sensorsprior to the run; a full time-dependent simulation is then run during,but independent of, the tool process. The obtained virtual measurementscan be displayed to and analyzed by the operator like any other actuallymeasured tool parameter. If the simulation runs faster than the waferprocess, then simulation results are known ahead of the correspondingactual measurements made during the wafer process. Knowing themeasurements ahead of time allows the implementation of variousfeed-forward control functions based on these measurements as will befurther described below.

In yet another embodiment of the process of FIG. 4, the first principlessimulation may be performed in a self correcting mode by comparingvirtual sensor measurements to corresponding physical sensormeasurements. For example, during the first run with a certain processrecipe/tool condition, the tool operator would use the “then best-knowninput parameters” for the model. During and after each simulation run,the simulation module(s) can compare the predicted “measurements” to theactual measurements, at locations where actual measurements fromphysical sensors, are made. If a significant difference is detected,optimization and statistical methods may be used to alter the input dataand/or the first principles physical model itself, until betteragreement of predicted and actual measured data is achieved. Dependingon the situation, these additional refinement simulation runs may bemade concurrent with the next wafer/wafer cassettes, or when the tool isoff-line. Once refined input parameters are known, they can be stored ina library for later use, eliminating the need for subsequent inputparameter and model refinements for the same process condition.Furthermore, refinements of the model and input data can be distributedvia the network setup of FIG. 3 to other tools, eliminating the need forself-correcting runs in those other tools.

Once the simulation is executed to provide a virtual sensor measurement,the virtual sensor measurement is used to facilitate a process performedby the semiconductor processing tool 102. For example, the virtualsensor measurements may be used as inputs to the tool control system forvarious purposes, such as comparison to actual sensor measurements,in-process recipe changes, fault detection and operator warnings,generation of databases of process conditions, refinement of models andinput data, etc. These are typical actions performed by a tool controlsystem based on measurements made by physical sensors. Use of virtualsensor measurement may be used to characterize or control a process aswill be described below. Moreover, the virtual sensor measurements maybe stored in libraries on computer storage media for later use,eliminating the need to repeat simulation runs with the same inputconditions, unless there has been a change in the model or inputconditions (e.g. during refinement, for example).

In addition to providing virtual sensor readings, the on-tool firstprinciples simulation capability of the present invention facilitatessemiconductor process development. More specifically, characterizationof a process on a tool by use of the current design-of-experimentsprocess development approach requires a different process run for eachvariation of the operating parameters, which results in time consumingand expensive characterization processes. The on-tool first principlessimulation capabilities of the present invention allow parametervariations and what-if analysis on the tool itself, and without actualprocess runs including variations of those process variables that arewell-modeled by the first-principles simulation. This allows a largereduction of the number of experiments needed to characterize a processon a tool.

FIG. 5 is a flow chart showing a process for using first principlessimulation techniques to characterize a process performed by asemiconductor processing tool in accordance with an embodiment of thepresent invention. The process shown in FIG. 5 may be run on the firstprinciples simulation processor 108 of FIG. 1, for example, or using thearchitecture of FIG. 3. As seen in FIG. 5, the process begins in step501 with inputting data for obtaining characterization informationrelating to a process performed by the semiconductor processing tool102. The data input in step 501 may be any of the data types describedwith respect to step 201 of FIG. 2, as long as the input data enables afirst principles simulation to provide simulation results used tocharacterize a process performed by the semiconductor processing tool.Thus, the input data may be data related to physical attributes of thetool/tool environment, a process performed by the tool on asemiconductor wafer, or the results of such process. Moreover, the inputdata of step 501 may be directly input from a physical sensor ormetrology tool coupled to the first principles simulation processor 104,or indirectly input from a manual input device or database. The data mayalso be input from a simulation module providing virtual sensor readingsas described with respect to FIG. 4. Where the data is indirectly inputby manual input device or a database, the data may be data that wasrecorded from a previously run process, such as sensor data from apreviously run process. Alternatively the data may be set by thesimulation operator as “best known input parameters” for the particularsimulations, which may or may not relate to the data collected during aprocess. The type of input data input by the processing tool generallydepends on the desired characterization data to be obtained.

In addition to inputting the input data, the first principles simulationprocessor 108 also inputs the first principles physical model forcharacterizing the process as shown by step 503. Step 503 includesinputting the physical attributes of the tool modeled by the model, aswell as the first principles fundamental equations typically codified insoftware necessary to perform a first principles simulation to obtaincharacterization data for the process performed by the semiconductorprocessing tool 102. The first principles physical model of step 503 maybe input to the processor from an external memory or an internal memorydevice integral to the processor. Moreover, while step 503 is shown inFIG. 5 as following step 501, it is to be understood that the firstprinciples simulation processor 108 may perform these stepssimultaneously or in reverse of the order shown in FIG. 5.

In step 505, a first principles simulation processor, such as theprocessor 108 of FIG. 1, uses the input data of step 501 and the firstprinciples physical model of step 503 to execute a first principlessimulation and provide a simulation result used to characterize theprocess. Step 505 may be performed either at a different time, orconcurrently with the process performed by the semiconductor processingtool. Simulations run not concurrently with the tool process may useinitial and boundary conditions stored from previous process runs withthe same or similar process conditions. As noted with respect to FIG. 2above, this is suitable in cases when the simulation runs slower thanthe wafer process, and time may be used between wafer cassettes and evenduring tool shutdowns for preventive maintenance, for example, to havethe simulation module solve for required simulation results.

Where the first principles simulation is run concurrently with theprocess performed by the semiconductor tool, the first principlessimulation may provide characterization data of a same or differentparameter tested by an experimental process performed by the simulation.For example, the first principles simulation may be performed to providevariations of a parameter being tested by a design of experimentsprocess performed by the semiconductor processing tool. Alternatively,the first principles simulation may provide characterization data for aparameter different than a parameter tested in an experiment performedon the semiconductor processing tool.

Once the simulation is executed in step 505, the simulation result isused as part of a data set to characterize the process performed by thesemiconductor processing tool as shown in step 507. As noted above, thisuse of simulation results as characterization data can significantlyreduce or eliminate the need for time consuming and expensiveexperiments required of the design of experiments approach tocharacterizing a process. The characterization data set may be stored ina library for use in a later process performed by the tool.

The on-tool first principles simulation capability of the presentinvention may also be used to provide fault detection and processcontrol. Existing methods for fault detection and process control of aprocess performed by the semiconductor processing tool are mostlystatistical in nature. These methods require experimental designmethodology that involves the burden of performing multiple process runswhile varying all of the operating parameters of the tool. The resultsof these process runs are recorded in a database that is used forlook-up, interpolation, extrapolation, sensitivity analysis, etc. inorder to sense or control a process of the semiconductor processingtool.

However, for these statistical methods to be able to reliably sense andcontrol the tool under widely varying operating conditions, the databasemust be broad-enough to cover all operating conditions, which makes thedatabase a burden to produce. The on-tool first principles simulationcapability of the present invention does not require the creation of anysuch database because tool response to process conditions is predictedfrom physical first principles directly and accurately, given accurateworking models and accurate input data. However, statistical methods canstill be used to refine working models and input data as more run-timeinformation under different operating conditions becomes available, buthaving such information is not required by the present invention forprocess sensing and control capability. Indeed, the process model canprovide a basis upon which the process can be empirically controlled byusing the process model to extend those known empirical solutions to“solutions” where empirical results have not been physically made.Hence, the present invention in one embodiment empirically characterizesthe process tool by supplementing the known (i.e. physically observed)solutions with first principle simulation module solutions, thesimulation module solutions being consistent with the known solutions.Eventually, as better statistics develop, the simulation modulesolutions can be superseded by the database of empirical solutions.

In one embodiment of the present invention, the on-tool first principlessimulation does not require the creation or access to a database becausetool response to process conditions is predicted directly from firstprinciples. Statistical methods may still be used to refine workingmodels and the input data, as more run time information under differentoperating conditions becomes available, but having such information isnot required in this embodiment for process sensing and control andfault detection.

FIG. 6 is a block diagram of a system for using first principlessimulation techniques to control a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention. As seen in this figure, the system includes a process tool602 coupled to an advanced process control (APC) infrastructure 604,which includes a simulation module 606, an APC controller 608, and alibrary 610. Also coupled to the APC infrastructure 604 is a metrologytool 612 and remote controller 614. As seen in FIG. 6, the library 610may include a solution database 616 and a grid database 618.

The process tool 602 may be implemented as the semiconductor processingtool 102 described with respect to FIG. 1. Thus, the process tool 602may be a material processing system, etch system, photoresist spincoating system, lithography system, dielectric coating system,deposition system, rapid thermal processing (RTP) system for thermalannealing, and/or batch diffusion furnace or other suitablesemiconductor manufacturing processing system, for example. As seen inFIG. 6, the process tool 602 provides tool data to the simulation module606 and receives control data from the APC controller 608 as will befurther described below. The process tool 602 is also coupled tometrology tool 612, which provides process results information to thesimulation module 606.

The simulation module 606 is a computer, workstation, or otherprocessing device capable of executing first principles simulationtechniques to control a process performed by the tool 602, and thereforemay be implemented as the simulation module 302 described with respectto FIG. 3. Thus, the simulation module 602 includes the first principlesphysical model 106 and the first principles simulation processor 108described with respect to FIG. 1, as well as any other hardware and/orsoftware that may be helpful for executing first principles simulationsto control a process. In the embodiment of FIG. 6, the simulation module606 is configured to receive tool data from one or more diagnostics onthe tool 602 for processing and subsequent use during simulation modelexecution. The tool data may include the aforementioned fluid mechanicdata, electrical data, chemical data, thermal, and mechanical data, orany of input data described with respect to FIGS. 1 and 2 above. In theembodiment of FIG. 6, the tool data can be utilized to determineboundary conditions and initial conditions for a model to be executed onthe simulation module 606. The model can, for example, include theaforementioned ANSYS, FLUENT, or CFD-ACE+ codes, to compute flow fields,electromagnetic fields, temperature fields, chemistry, surface chemistry(i.e. etch surface chemistry or deposition surface chemistry), etc. Themodels developed from first principles can resolve details within theprocessing system in order to provide an input for process control ofthe tool.

The APC controller 608 is coupled to the simulation module 606 in orderto receive a simulation result from the simulation module 606 and toutilize the simulation result to implement a control methodology forprocess adjustment/correction of a process performed on the tool 602.For example, an adjustment can be made to correct processnon-uniformities. In one embodiment of the present invention, one ormore perturbation solutions are executed on the simulation module 606,centered on a process solution for a process currently run on theprocess tool 602. The perturbation solutions can then be utilized with,for instance, a nonlinear optimization scheme such as the method ofsteepest descent (Numerical Methods, Dahlquist & Bjorck, Prentice-Hall,Inc., Englewood Cliffs, N.J., 1974, p. 441; Numerical Recipes, Press etal., Cambridge University Press, Cambridge, 1989, pp. 289-306) todetermine a direction within an n-dimensional space for applying thecorrection. The correction can then be implemented on the process tool602 by the APC controller 608. For example, at least one of tool data(i.e. physical sensor data), or results from a current execution of thesimulation can indicate that the processing system exhibits anon-uniform static pressure field overlying the substrate given thecurrent initial/boundary conditions. The non-uniformity can, in turn,contribute to an observed non-uniformity of a metric used to quantifythe performance of the substrate process, measured by the metrologytool, on the substrate, i.e. a critical dimension, feature depth, filmthickness, etc. By perturbing the input parameters to the currentexecution of the simulation, a set of perturbation solutions can beobtained in order to determine the best “route” to take in order toremove, or reduce, the static pressure non-uniformity. For example, theinput parameters for the process can include a pressure, a power(delivered to an electrode for generating plasma), a gas flow rate, etc.While perturbing one input parameter at a time and holding all otherinput parameters constant, a sensitivity matrix can be formed that maybe employed with the above identified optimization scheme to derive acorrection suitable for correcting the process non-uniformity.

In another embodiment of the present invention, the simulation resultsare utilized in conjunction with a principal components analysis (PCA)model formulated as described in pending U.S. patent application Ser.No. 60/343174, entitled “Method of detecting, identifying, andcorrecting process performance,” the contents of which are incorporatedherein by reference. Therein, a relationship can be determined between asimulated signature (i.e. spatial components of the simulation modelresults) and a set of at least one controllable process parameter usingmultivariate analysis (i.e. PCA). This relationship can be utilized toimprove the data profile corresponding to a process performanceparameter (i.e., a model result). The principle components analysisdetermines a relationship between spatial components of a result (orpredicted output) of a simulation of the semiconductor processing tooland a set of at least one control variable (or input parameter). Thedetermined relationship is utilized to determine a correction to the atleast one control variable (or input parameter) in order to cause aminimization of the magnitude of the spatial components in order toimprove (or decrease) the non-uniformity of the simulated result (ormeasured result if available).

As noted above, the library 610 coupled to the simulation module 606 inFIG. 6 is configured to include a solution database 616 and a griddatabase 618. The solution database 616 can include a coarsen-dimensional database of solutions, whereby the order n of then-dimensional space is governed by the number of independent parametersfor the given solution algorithm. When the simulation module 606retrieves the tool data for a given process run, the library 610 can besearched based upon model input to determine the closest fittingsolution. This solution can be used according to the present inventionas an initial condition for subsequent first principles simulation,thereby reducing the number of iterations required to be performed bythe simulation module to provide a simulation result. With each modelexecution, the new solution can be added to the solution database 616.Additionally, the grid database 618 can include one or more grid sets,whereby each grid set addresses a given process tool or process toolgeometry. Each grid set can include one or more grids with differentgrid resolutions, ranging from coarse to fine. The selection of gridscan be utilized to reduce solution time by performing multi-gridsolution techniques (i.e. solve for a simulation result on coarse grid,followed by solution on finer grid, finest grid, etc.).

The metrology tool 612 may be configured to measure process performanceparameters such as: etch rate, deposition rate, etch selectivity (ratioof the rate at which a first material is etched to the rate at which asecond material is etched), an etch critical dimension (e.g. length orwidth of feature), an etch feature anisotropy (e.g. etch featuresidewall profile), a film property (e.g. film stress, porosity, etc.), amask (e.g. photoresist) film thickness, a mask (e.g. photoresist)pattern critical dimension, or any other parameter of a processperformed by the semiconductor processing tool. The remote controller612 exchanges information with the simulation module 606 including modelsolver parameters (i.e. solver parameter updates), solution status,model solutions, and solution convergence history.

FIG. 7 is a flow chart showing a process for using first principlessimulation techniques to control a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention. The flow chart is presented beginning with step 702 forprocessing a substrate or batch of substrates within a process tool,such as the process tool 602. At step 704, tool data is measured andprovided as input to a simulation module such as simulation module 606.Boundary conditions and initial conditions are then imposed on the firstprinciples physical model of the simulation module to set-up the modelas shown in step 706. At step 708, the first principles physical modelis executed to provide first principles simulation results that areoutput to a controller such as the APC controller 608 of FIG. 6. Thecontroller then determines a control signal from the simulation resultas shown in step 710. At any time, for example, from run-to-run orbatch-to-batch, the operator has the opportunity to select the controlalgorithm to be employed within the APC controller 608. For example, theAPC controller can utilize either the process model perturbationresults, or the PCA model results. In either run-to-run orbatch-to-batch, the process can be adjusted/corrected by the controllerusing simulation results as depicted in step 712.

In another embodiment of the present invention, an empirical model maybe used in conjunction with the first principles simulation to providecontrol of a process performed by the process tool. FIG. 8 is a blockdiagram of a system for using first principles simulation techniques andan empirical model to control a process performed by a semiconductorprocessing tool in accordance with an embodiment of the presentinvention. As seen in this figure, the system includes a process tool802 coupled to an advanced process control (APC) infrastructure 804,which includes a simulation module 806 and an APC controller 808. Alsocoupled to the APC infrastructure 804 is a metrology tool 812 and remotecontroller 814. These items are similar to those corresponding itemsdiscussed with respect to FIG. 6, except the items of FIG. 8 are furtherconfigured to function in consideration of an empirical model. Thus,these similar items are not described with respect to FIG. 8.

As seen in FIG. 8, the system includes a model analysis processor 840,which is coupled to the simulation module 806 and configured to receivea simulation result from the module 806. In the embodiment of FIG. 8,model analysis includes the construction of an empirical model fromnon-dimensionalization of the simulation results. As simulation resultsare received on a run-to-run or batch-to-batch basis, an empirical modelis constructed and stored in the empirical model library 842. Forexample, the process tool 802 undergoes a history of process cyclesranging from process development through yield ramp to volumeproduction. During these process cycles, a process chamber of the toolevolves from a “clean” chamber through chamber qualification andseasoning, to an “aged” chamber preceding chamber cleaning andmaintenance. After several maintenance cycles, an empirical model canevolve to include a statistically sufficient sample of the parameterspace corresponding to the specific process tool and process associatedtherewith. In other words, through cleaning cycles, process cycles, andmaintenance cycles, the tool 802 (with the aid of the simulation module)inherently determines the bounds of the parameter space. Ultimately, theevolved empirical model stored in library 842 can supersede thegenerally more intensive process model based on first principlessimulation, and can provide input to the APC controller for processadjustment/correction.

As seen in FIG. 8, the remote controller 814 can be coupled to theempirical model library 842 in order to monitor the evolution of theempirical model, and to enact decisions for overriding the simulationmodule controller input and opting for an empirical model controllerinput. Also, metrology tool 814 can be coupled to the empirical modeldatabase (connection not shown) in order to, similarly, provide input tothe empirical model database for calibration.

FIG. 9 is a flow chart showing a process for using first principlessimulation techniques and an empirical model to control a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention. The flow chart is presentedbeginning with step 902 for processing a substrate or batch ofsubstrates within a process tool, such as the process tool 802. At step904, tool data is measured and provided as input to a simulation modulesuch as simulation module 806. Boundary conditions and initialconditions are then imposed on the first principles physical model ofthe simulation module to set-up the model as shown in step 906. At step908, the first principles physical model is executed to perform firstprinciples simulation results that are output for analysis andconstruction of an empirical model, as depicted in step 910.

At any time, for example from run-to-run or from batch-to-batch, theoperator has the opportunity to select process control based on thefirst principles simulation or the empirical model. At some point in thebuilding of the empirical model, the operator may select to override thefirst principles simulation altogether in favor of the empirical modelwhich at that point can use a library of data andinterpolation/extrapolation schemes to rapidly extract controller inputfor a given set of tool data. Thus, decision block 912 determineswhether the first principles simulation or the empirical model is usedto control the process. Where no override is determined in step 912, theprocess continues at step 914 with the APC controller determining acontrol signal from the simulation result. Where model override isselected, the APC controller determines a control signal from theempirical model as shown in step 916. In another embodiment, acombination of first principles simulation results and empiricalmodeling can be used by the APC controller to control the process. Asshown by step 918, the process can be adjusted/corrected by thecontroller using either the model output shown in step 914 or theempirical model output shown in step 916. Thus, the process of FIG. 9shows a method of in-situ construction of an empirical model, and, oncestatistically significant, the empirical model can override thecomputationally intensive simulation process model. During processcontrol, a filter, such as an exponentially weighted moving average(EWMA) filter, can be employed in order to impart only a fraction of therequested correction. For example, the application of the filter cantake the form X_(new)=(1−λ)X_(old)+λ(X_(predicted)−X_(old)), whereinX_(new) is the new value for the given input parameter (controlvariable), X_(old) is the old (or previously used) value for the giveninput parameter, X_(predicted) is the predicted value for the inputparameter based upon one of the above described techniques, and λ is thefilter coefficient ranging from 0 to 1.

In yet another embodiment of the present invention, a faultdetector/classifier may be used in conjunction with the first principlessimulation to provide control of a process performed by the processtool. FIG. 10 is a block diagram of a system for using first principlessimulation techniques and a fault detector to control a processperformed by a semiconductor processing tool in accordance with anembodiment of the present invention. As seen in this figure, the systemincludes a process tool 1002 coupled to an advanced process control(APC) infrastructure 1004, which includes a simulation module 1006 andan APC controller 1008 and library 1010. While not shown in FIG. 10, thelibrary 1010 includes a solutions database and a grid database. Alsocoupled to the APC infrastructure 1004 is a metrology tool 1012 andremote controller 1014. These items are similar to those correspondingitems discussed with respect to FIG. 6, except the items of FIG. 10 arefurther configured to function in consideration of fault detection.Thus, these similar items are not described with respect to FIG. 10.

As seen in FIG. 10, the system includes a fault detector 1040 coupled tothe simulation module 1006, and configured to receive a simulationresult from the module 1006. For example, the output of the simulationmodule 1006 can include a profile of data. The profile of data can thenserve as input to multivariate analysis such as partial least squares(PLS) performed in the fault detection device 1040. In the PLS analysis,a set of loading (or correlation) coefficients can be defined whichrelate tool perturbation data ( X) to process performance data ( Y)describing a difference between simulated results Ysim and actualresults Yreal.

For example, using PLS, observation sets of tool perturbation data arereceived by the fault detector 1040 from the simulation module. Eitherthe tool perturbation data is determined in-situ, centered on thecurrent model solution, or determined a priori within the n-dimensionalsolution space using the process model. The order (n) of then-dimensional parameter space pertains to the number of independentparameters in the solution space (i.e. pressures, mass flow rates,temperatures, etc.; see below).

For a given perturbation set, the respective perturbation derivatives(i.e. ∂Y/∂v1, ∂Y/∂v2, ∂Y/∂v3; where v1, v2, v3 are different independentparameters) are stored within matrix X. For each observation set, toolperturbation data can be stored as a column in a matrix X and processperformance data (i.e. Ysim-Yreal) can be stored as a column in matrixY. Hence, once the matrix X is assembled, each row represents adifferent perturbation observation and each column represents adifferent tool data parameter. Once the matrix Y is assembled, each rowrepresents a different observation and each column represents adifferent process performance parameter. In general, matrix X can be anm by n matrix, and matrix Y can be an m by p matrix. Once all of thedata is stored in the matrices, the data can be mean-centered and/ornormalized, if desired. The process of mean-centering the data stored ina matrix column involves computing a mean value of the column elementsand subtracting the mean value from each element. Moreover, the dataresiding in a column of the matrix can be normalized by the standarddeviation of the data in the column.

In general, for multivariate analysis, the relationship between the tooldata and the process performance data can be expressed as follows:XB= Y;  (1)where X represents the m×n matrix described above, B represents an n×p(p<n) loading (or correlation) matrix and Y represents the m×p matrixdescribed above. Once the data matrices X and Y are assembled, arelationship designed to best approximate the X and Y spaces and tomaximize the correlation between X and Y is established using PLSanalysis.

In the PLS analysis model, the matrices X and Y are decomposed asfollows:X= TP ^(T) +Ē;  (2a)Y= UC ^(T) + F;  (2b)andŪ= T+ H;  (2c)where T is a matrix of scores that summarizes the X variables, P is amatrix of loadings for matrix X, Ū is a matrix of scores that summarizesthe Y variables, C is a matrix of weights expressing the correlationbetween Y and T( X), and Ē, F and H are matrices of residuals.Furthermore, in the PLS analysis model, there are additional loadings Wcalled weights that correlate Ū and X, and are used to calculate T.

In summary, the PLS analysis geometrically corresponds to fitting aline, plane or hyper plane to both the X and Y data represented aspoints in a multidimensional space, with the objective of closelyapproximating the original data tables X and Y, and maximizing thecovariance between the observation positions on the hyper planes.

FIG. 11 provides a schematic representation of the data inputs, X and Y,to the PLS analysis and the corresponding outputs T, P, Ū, C, W, Ē, F, Hand variable importance in the projection (VIP). An example of acommercially available software which supports PLS analysis modeling isPLS_Toolbox offered with MATLAB (commercially available from TheMathworks, Inc., Natick, Mass.), or SIMCA-P 8.0 (commercially availablefrom Umetrics, Kinnelon, N.J.). For instance, further details on thissoftware are provided in the User's Manual User Guide to SIMCA-P 8.0: Anew standard in multivariate data analysis is Umetrics AB, Version 8.0,which is also suitable for the present invention. Once a matrix isformulated, a matrix X is determined for each simulation result. Anydifference between the simulated result and the actual result can bedetermined and attributed to a specific (independent) process parameterusing the PLS analysis and the VIP result. For example, the maximum VIPvalue output from the PLS model corresponds to the process parametermost likely responsible for the difference.

FIG. 12 is a flow chart showing a process for using first principlessimulation techniques to detect a fault and control a process performedby a semiconductor processing tool in accordance with an embodiment ofthe present invention. The flow chart is presented beginning with step1202 for processing a substrate or batch of substrates within a processtool, such as the process tool 1002. At step 1204, tool data is measuredand provided as input to a simulation module such as simulation module1006. Boundary conditions and initial conditions are then imposed on thephysical model of the simulation module to set-up the model as shown instep 1206. At step 1208, the first principles physical model is executedto perform first principles simulation results that are output to acontroller such as the APC controller 1008 of FIG. 10. At any time, forexample, from run-to-run or batch-to-batch, the operator has theopportunity to select the control model to be employed within the APCcontroller. For example, the APC controller can utilize either theprocess model perturbation results, or the PCA model results. In eitherrun-to-run or batch-to-batch, the process can be adjusted/corrected bythe controller using model output. At step 1010, the process modeloutput serves as input to the PLS model in the fault detector 1040,permitting a fault to be detected and classified at step 1012. Forexample, as described above, a difference between the real processperformance Yreal and the simulated (or predicted) process performancefor the given process condition (i.e. set of input control variables)Ysim can be utilized to determine the existence of a process fault,wherein Yreal is measured using either a physical sensor, or a metrologytool, and Ysim is determined by executing a simulation provided theinput for the current process condition. If the difference (or variance,root mean square, or other statistic) between the real and simulatedresults exceeds a predetermined threshold, then a fault can be predictedto have occurred. The predetermined threshold can, for example, comprisea fraction of the mean value for the specific data, i.e. 5%, 10%, 15%,or it can be a multiple of a root mean square of the data, i.e. 1σ, 2σ,3σ. Once a fault is detected, it can be classified using PLS analysis.For example, a sensitivity matrix X has been determined (and, possibly,stored in library 1010) for a given input condition (i.e. set of inputcontrol variables). Either the tool perturbation data (sensitivitymatrix) is determined in-situ, centered on the current model solution,or determined a priori within the n-dimensional solution space using theprocess model. Using the sensitivity matrix and the difference betweenthe real and simulated results, equation (1) can be solved using PLSanalysis to identify those control variables (input parameters) thatexhibit the greatest correlation with the observed difference betweenthe real and simulated results. Using the example provided above, theprocess performance may be summarized by a profile of static pressureacross the space overlying the substrate. The real result Yrealrepresents the measured profile of pressure, and Ysim represents thesimulated profile of pressure. Let's assume a gas flow rate is set,however, the mass flow controller doubles the flow rate (yet reports theset value). One would expect to see a difference between the simulatedand measured (real) profiles of pressure; i.e. the flow rate is off by afactor of two between the real and simulated cases. The differencebetween the real and simulated results would be sufficiently large toexceed a pre-determined threshold. Using the PLS analysis, thoseparameters which tend to affect the profile in pressure the greatestwould be identified, such as a gas flow rate. The presence of a faultand its characterization can be reported to an operator as process toolfault status, or can cause the APC controller to perform control of theprocess tool (such as shut down) in response to the fault detection.

FIG. 13 is a block diagram of a vacuum processing system, to which aprocess control embodiment of the present invention may be applied. Thevacuum processing system depicted in FIG. 13 is provided forillustrative purposes and does not limit the scope of the presentinvention in any way. The vacuum processing system includes a processtool 1302 having a substrate holder 1304 for supporting a substrate1305, a gas injection system 1306, and a vacuum pumping system. The gasinjection system 1306 can include a gas inject plate, a gas injectionplenum, and one or more gas injection baffle plates within the gasinjection plenum. The gas injection plenum can be coupled to one or moregas supplies such as gas A and gas B, wherein the mass flow rate of gasA and gas B into the processing system is affected by two mass flowcontrollers MFCA 1308 and MFCB 1310. Furthermore, a pressure sensor 1312for measuring a pressure P1 can be coupled to the gas injection plenum.The substrate holder can, for example, include a plurality of componentsincluding but not limited to a helium gas supply for improving thegas-gap thermal conductance between the substrate and the substrateholder, an electrostatic clamping system, temperature control elementsincluding cooling elements and heating elements, and lift pins forlifting the substrate to and from the surface of the substrate holder.Additionally, the substrate holder can include a temperature sensor 1314for measuring the substrate holder temperature (T1) or substratetemperature, and a temperature sensor 1316 for measuring the coolanttemperature (T3). As described above, helium gas is supplied to thebackside of the substrate, wherein the gas-gap pressure (P(He)) can bevaried at one or more locations. Furthermore, another pressure sensor1318 can be coupled to the process tool to measure chamber pressure(P2), another temperature sensor 1320 can be coupled to the process toolto measure a surface temperature (T2), and another pressure sensor 1322can be coupled to the inlet of the vacuum pumping system to measure aninlet pressure (P3).

A diagnostic controller 1324 can be coupled to each of the sensorsdescribed above and can be configured to provide measurements from thesesensors to the simulation module described above. For the exemplarysystem of FIG. 13, the model executed on the simulation module can, forexample, include three components, namely, a thermal component, a gasdynamic component, and a chemistry component. In the first component,the gas-gap pressure field can be determined, followed by a calculationof the gas-gap thermal conductance. Thereafter, the spatially resolvedtemperature field for the substrate (and substrate holder) can bedetermined by properly setting boundary conditions (and internalconditions) such as boundary temperature, or boundary heat flux, powerdeposited in resistance heating elements, power removed in coolingelements, heat flux at substrate surface due to the presence of plasma,etc.

In one example of the present invention, ANSYS is utilized to computethe temperature field. Utilizing the second component of the processmodel, (i.e. the gas dynamic component), the gas pressure field andvelocity field can be determined using the surface temperatures computedin the thermal component, and several of the aforementionedmeasurements. For example, the mass flow rate and pressure (P1) can beutilized to determine an inlet condition, and the pressure (P3) can beutilized to determine an outlet condition, and CFD-ACE+ can be utilizedto compute the gas pressure and velocity fields. Utilizing the chemistrymodel (i.e., the third component), the previously computed velocity,pressure, and temperature fields can be utilized as inputs to achemistry model to compute, for example, an etch rate. Depending on thecomplexity of the process tool geometry, each of these model componentscan be executed on a time scale within a batch-to-batch process cycle.Any one of these components can, for example, be utilized to providespatial uniformity data as input to the process control, methodology,process characterization, and/or fault detection/classification.

From the derived models and analysis of the process in response tochanges in processing conditions and/or effects such as reactor aging,an empirical model can be assimilated over time. As such, when thenumber of repetitions on the reactor becomes statistically significantas determined by standard statistical analysis programs, the processcontrol evolves to a control which is empirically based for thoseprocesses which are essentially “repeats” of previously run operations.Yet, according to the present invention, the process control returns thecapability to perform first-principles simulation if necessary toaccommodate new processes or alterations in the process geometry.

FIG. 14 illustrates a computer system 1401 upon which an embodiment ofthe present invention may be implemented. The computer system 1401 maybe used as the first principles simulation processor 108 to perform anyor all of the functions of the first principles simulation processordescribed above, or may be used as any other device, or to perform anyprocess step described with respect to FIGS. 1-13. The computer system1401 includes a bus 1402 or other communication mechanism forcommunicating information, and a processor 1403 coupled with the bus1402 for processing the information. The computer system 1401 alsoincludes a main memory 1404, such as a random access memory (RAM) orother dynamic storage device (e.g., dynamic RAM (DRAM), static RAM(SRAM), and synchronous DRAM (SDRAM)), coupled to the bus 1402 forstoring information and instructions to be executed by processor 1403.In addition, the main memory 1404 may be used for storing temporaryvariables or other intermediate information during the execution ofinstructions by the processor 1403. The computer system 1401 furtherincludes a read only memory (ROM) 1405 or other static storage device(e.g., programmable ROM (PROM), erasable PROM (EPROM), and electricallyerasable PROM (EEPROM)) coupled to the bus 1402 for storing staticinformation and instructions for the processor 1403.

The computer system 1401 also includes a disk controller 1406 coupled tothe bus 1402 to control one or more storage devices for storinginformation and instructions, such as a magnetic hard disk 1407, and aremovable media drive 1408 (e.g., floppy disk drive, read-only compactdisc drive, read/write compact disc drive, compact disc jukebox, tapedrive, and removable magneto-optical drive). The storage devices may beadded to the computer system 1401 using an appropriate device interface(e.g., small computer system interface (SCSI), integrated deviceelectronics (IDE), enhanced-IDE (E-IDE), direct memory access (DMA), orultra-DMA).

The computer system 1401 may also include special purpose logic devices(e.g., application specific integrated circuits (ASICs)) or configurablelogic devices (e.g., simple programmable logic devices (SPLDs), complexprogrammable logic devices (CPLDs), and field programmable gate arrays(FPGAs)).

The computer system 1401 may also include a display controller 1409coupled to the bus 1402 to control a display 1410, such as a cathode raytube (CRT), for displaying information to a computer user. The computersystem includes input devices, such as a keyboard 1411 and a pointingdevice 1412, for interacting with a computer user and providinginformation to the processor 1403. The pointing device 1412, forexample, may be a mouse, a trackball, or a pointing stick forcommunicating direction information and command selections to theprocessor 1403 and for controlling cursor movement on the display 1410.In addition, a printer may provide printed listings of data storedand/or generated by the computer system 1401.

The computer system 1401 performs a portion or all of the processingsteps of the invention in response to the processor 1403 executing oneor more sequences of one or more instructions contained in a memory,such as the main memory 1404. Such instructions may be read into themain memory 1404 from another computer readable medium, such as a harddisk 1407 or a removable media drive 1408. One or more processors in amulti-processing arrangement may also be employed to execute thesequences of instructions contained in main memory 1404. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions. Thus, embodiments are notlimited to any specific combination of hardware circuitry and software.

As stated above, the computer system 1401 includes at least one computerreadable medium or memory for holding instructions programmed accordingto the teachings of the invention and for containing data structures,tables, records, or other data described herein. Examples of computerreadable media are compact discs, hard disks, floppy disks, tape,magneto-optical disks, PROMs (EPROM, EEPROM, flash EPROM), DRAM, SRAM,SDRAM, or any other magnetic medium, compact discs (e.g., CD-ROM), orany other optical medium, punch cards, paper tape, or other physicalmedium with patterns of holes, a carrier wave (described below), or anyother medium from which a computer can read.

Stored on any one or on a combination of computer readable media, thepresent invention includes software for controlling the computer system1401, for driving a device or devices for implementing the invention,and for enabling the computer system 1401 to interact with a human user(e.g., print production personnel). Such software may include, but isnot limited to, device drivers, operating systems, development tools,and applications software. Such computer readable media further includesthe computer program product of the present invention for performing allor a portion (if processing is distributed) of the processing performedin implementing the invention.

The computer code devices of the present invention may be anyinterpretable or executable code mechanism, including but not limited toscripts, interpretable programs, dynamic link libraries (DLLs), Javaclasses, and complete executable programs. Moreover, parts of theprocessing of the present invention may be distributed for betterperformance, reliability, and/or cost.

The term “computer readable medium” as used herein refers to any mediumthat participates in providing instructions to the processor 1403 forexecution. A computer readable medium may take many forms, including butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-volatile media includes, for example, optical, magneticdisks, and magneto-optical disks, such as the hard disk 1407 or theremovable media drive 1408. Volatile media includes dynamic memory, suchas the main memory 1404. Transmission media includes coaxial cables,copper wire and fiber optics, including the wires that make up the bus1402. Transmission media also may also take the form of acoustic orlight waves, such as those generated during radio wave and infrared datacommunications.

Various forms of computer readable media may be involved in carrying outone or more sequences of one or more instructions to processor 1403 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions for implementing all or a portion of the present inventionremotely into a dynamic memory and send the instructions over atelephone line using a modem. A modem local to the computer system 1401may receive the data on the telephone line and use an infraredtransmitter to convert the data to an infrared signal. An infrareddetector coupled to the bus 1402 can receive the data carried in theinfrared signal and place the data on the bus 1402. The bus 1402 carriesthe data to the main memory 1404, from which the processor 1403retrieves and executes the instructions. The instructions received bythe main memory 1404 may optionally be stored on storage device 1407 or1408 either before or after execution by processor 1403.

The computer system 1401 also includes a communication interface 1413coupled to the bus 1402. The communication interface 1413 provides atwo-way data communication coupling to a network link 1414 that isconnected to, for example, a local area network (LAN) 1415, or toanother communications network 1416 such as the Internet. For example,the communication interface 1413 may be a network interface card toattach to any packet switched LAN. As another example, the communicationinterface 1413 may be an asymmetrical digital subscriber line (ADSL)card, an integrated services digital network (ISDN) card or a modem toprovide a data communication connection to a corresponding type ofcommunications line. Wireless links may also be implemented. In any suchimplementation, the communication interface 1413 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

The network link 1414 typically provides data communication through oneor more networks to other data devices. For example, the network link1414 may provide a connection to another computer through a localnetwork 1415 (e.g., a LAN) or through equipment operated by a serviceprovider, which provides communication services through a communicationsnetwork 1416. The local network 1414 and the communications network 1416use, for example, electrical, electromagnetic, or optical signals thatcarry digital data streams, and the associated physical layer (e.g., CAT5 cable, coaxial cable, optical fiber, etc). The signals through thevarious networks and the signals on the network link 1414 and throughthe communication interface 1413, which carry the digital data to andfrom the computer system 1401 maybe implemented in baseband signals, orcarrier wave based signals. The baseband signals convey the digital dataas unmodulated electrical pulses that are descriptive of a stream ofdigital data bits, where the term “bits” is to be construed broadly tomean symbol, where each symbol conveys at least one or more informationbits. The digital data may also be used to modulate a carrier wave, suchas with amplitude, phase and/or frequency shift keyed signals that arepropagated over a conductive media, or transmitted as electromagneticwaves through a propagation medium. Thus, the digital data may be sentas unmodulated baseband data through a “wired” communication channeland/or sent within a predetermined frequency band, different thanbaseband, by modulating a carrier wave. The computer system 1401 cantransmit and receive data, including program code, through thenetwork(s) 1415 and 1416, the network link 1414, and the communicationinterface 1413. Moreover, the network link 1414 may provide a connectionthrough a LAN 1415 to a mobile device 1417 such as a personal digitalassistant (PDA) laptop computer, or cellular telephone.

Numerous modifications and variations of the present invention arepossible in light of the above teachings. It is therefore to beunderstood that within the scope of the appended claims, the inventionmay be practiced otherwise than as specifically described herein. Forexample, the process steps described herein and recited in the claimsmay be performed simultaneously or in a sequence other than the sequencein which they are described or listed herein. As should be understood byone of ordinary skill in the art, only those process steps necessary tothe performance of a later process steps are required to be performedbefore the later process step is performed.

1. A method of facilitating a process performed by a semiconductorprocessing tool, comprising: inputting a first principles physical modelincluding a set of computer-encoded differential equations, the firstprinciples physical model describing at least one of a basic physical orchemical attribute of the semiconductor processing tool and including 1)a spatially resolved model of a physical geometry of the semiconductorprocessing tool and 2) a grid set addressing the semiconductorprocessing tool or a geometry of the semiconductor processing tool;inputting process data related to an actual process being performed bythe semiconductor processing tool; setting boundary conditions for thespatially resolved model of a physical geometry of the semiconductorprocessing tool based on said process data related to the actual processbeing performed by the semiconductor processing tool; storing in afab-level library known simulation results obtained from simulationmodules in a device manufacturing fab and distributing the knownsimulation results to other semiconductor processing tools in the devicemanufacturing fab; solving the computer-encoded differential equationsof the first principles physical model for the spatially resolved modelconcurrently with the actual process being performed by: and in a timeframe shorter in time than the actual process being performed by: usingcode parallelization techniques on multiple simulation modules in thedevice manufacturing fab, and re-using known simulation solutions asinitial conditions for the first principles simulation, wherein re-usingknown simulation solutions comprises searching in the fab-level libraryfor a closest fitting solution which if used for the initial conditionwould reduce the number of iterations required by the simulation module;providing from the solution of the computer-encoded differentialequations solved concurrently with the actual process being performed afirst principles simulation result; and using the simulation results aspart of a data set that characterizes the actual process being performedby the semiconductor processing tool.
 2. The method of claim 1, whereinsaid inputting process data comprises directly inputting the datarelating to the actual process being performed by the semiconductorprocessing tool from at least one of a physical sensor and a metrologytool physically mounted on the semiconductor processing tool.
 3. Themethod of claim 1, wherein said inputting process data comprisesindirectly inputting the data relating to the actual process beingperformed by the semiconductor processing tool from at least one of amanual input device and a database.
 4. The method of claim 3, whereinsaid indirectly inputting comprises inputting data recorded from aprocess previously performed by the semiconductor processing tool. 5.The method of claim 3, wherein said indirectly inputting comprisesinputting data set by a simulation operator.
 6. The method of claim 1,wherein said inputting process data comprises inputting the datarelating to a process performed by the semiconductor processing tool asvirtual sensor data from a simulation module.
 7. The method of claim 1,wherein said inputting process data comprises inputting data relating toat least one of the physical characteristics of the semiconductorprocessing tool and the semiconductor tool environment.
 8. The method ofclaim 1, wherein said inputting data comprises inputting data relatingto at least one of a characteristic and a result of a process performedby the semiconductor processing tool.
 9. The method of claim 1, whereinsaid inputting a first principles physical model comprises inputtingfundamental equations necessary to perform first principles simulationto obtain a simulation result that can form part of a data set thatcharacterizes the process performed by the semiconductor processingtool.
 10. The method of claim 1, wherein said performing firstprinciples simulation comprises performing first principles simulationto provide a simulation result that is a variation of a parameter testedby the concurrent process performed by the semiconductor processingtool.
 11. The method of claim 1, wherein said performing firstprinciples simulation comprises performing first principles simulationto provide a simulation result relating to a different parameter than aparameter tested by the concurrent process performed by thesemiconductor processing tool.
 12. The method of claim 1, furthercomprising storing the data set in a library for subsequent useprocesses performed by the semiconductor processing tool.
 13. The methodof claim 1, further comprising using a network of interconnectedresources to perform at least one of the process steps recited inclaim
 1. 14. The method of claim 13, further comprising using codeparallelization among interconnected computational resources to sharethe computational load of the first principles simulation.
 15. Themethod of claim 13, further comprising sharing simulation informationamong interconnected resources to facilitate a process performed by thesemiconductor processing tool.
 16. The method of claim 15, wherein saidsharing simulation information comprises distributing simulation resultsamong the interconnected resources to reduce redundant execution ofsubstantially similar first principles simulations by differentresources.
 17. The method of claim 15, wherein said sharing simulationinformation comprises distributing model changes among theinterconnected resources to reduce redundant refinements of firstprinciples simulations by different resources.
 18. The method of claim15, further comprising using remote resources via a wide area network tofacilitate the semiconductor process performed by the semiconductorprocessing tool.
 19. The method of claim 18, wherein said using remoteresources comprises using at least one of remote computational andstorage resources via a wide area network to facilitate thesemiconductor process performed by the semiconductor processing tool.20. A system comprising: a semiconductor processing tool configured toperform an actual process; a fab-level library storing known simulationresults obtained from simulation modules in a device manufacturing fab;a fab-level process controller distributing the known simulation resultsto other semiconductor processing tools in the device manufacturing fab;a first principles simulation processor configured to input a firstprinciples physical model including a set of computer-encodeddifferential equations describing at least one of a basic physical orchemical attribute the semiconductor processing tool and including 1) aspatially resolved model of a physical geometry of the semiconductorprocessing tool and 2) a grid set addressing the semiconductorprocessing tool or a geometry of the semiconductor processing tool; aninput device configured to input process data related to an actualprocess being performed by the semiconductor processing tool; and saidfirst principles simulation processor further configured to: setboundary conditions for the spatially resolved model of a physicalgeometry of the semiconductor processing tool based on said process datarelated to the actual process being performed by the semiconductorprocessing tool, solve the computer-encoded differential equations ofthe first principles physical model for the spatially resolved modelconcurrently with the actual process being performed by: and in a timeframe shorter in time than the actual process being performed by: usingcode parallelization techniques on multiple simulation modules in thedevice manufacturing fab, and re-using known simulation solutions asinitial conditions for the first principles simulation, wherein re-usingknow simulation solutions comprises searching in the fab-level libraryfor a closest fitting solution which if used for the initial conditionwould reduce the number of iterations required by the simulation module,provide from the solution of the computer-encoded differential equationssolved concurrently with the actual process being performed a firstprinciples simulation result, and wherein the simulation result is usedas part of a data set that characterizes the process performed by thesemiconductor processing tool.
 21. The system of claim 20, wherein saidinput device comprises at least one of a physical sensor and a metrologytool physically mounted on the semiconductor processing tool.
 22. Thesystem of claim 20, wherein said input device comprises at least one ofa manual input device and a database.
 23. The system of claim 22,wherein said input device is configured to input data recorded from aprocess previously performed by the semiconductor processing tool. 24.The system of claim 22, wherein said input device is configured to inputdata set by a simulation operator.
 25. The system of claim 20, whereinsaid input device is configured to input the data relating to a processperformed by the semiconductor processing tool as virtual sensor datafrom a simulation module.
 26. The system of claim 20, wherein said inputdevice is configured to input data relating to at least one of thephysical characteristics of the semiconductor processing tool and thesemiconductor tool environment.
 27. The system of claim 20, wherein saidinput device is configured to input data relating to at least one of acharacteristic and a result of a process performed by the semiconductorprocessing tool.
 28. The system of claim 20, wherein said processor isconfigured to input a first principles physical model comprisingfundamental equations necessary to perform first principles simulationto obtain a simulation result that can form part of a data set thatcharacterizes the process performed by the semiconductor processingtool.
 29. The system of claim 20, wherein said processor is configuredto perform said first principles simulation concurrently with theprocess performed by the semiconductor processing tool.
 30. The systemof claim 20, wherein said processor is configured to perform the firstprinciples simulation to provide a simulation result that is a variationof a parameter tested by the concurrent process performed by thesemiconductor processing tool.
 31. The system of claim 20, wherein saidprocessor is further configured to store the data set in a library forsubsequent use processes performed by the semiconductor processing tool.32. The system of claim 20, further comprising a network ofinterconnected resources connected to said processor and configured toassist said processor in performing at least one of the inputting afirst principles simulation model and performing a first principlessimulation.
 33. The system of claim 32, wherein said network ofinterconnected resources is configured to use code parallelization withsaid processor to share the computational load of the first principlessimulation.
 34. The system of claim 32, wherein said network ofinterconnected resources is configured to share simulation informationwith said processor to facilitate said process performed by thesemiconductor processing tool.
 35. The system of claim 34, wherein saidnetwork of interconnected resources is configured to distributesimulation results to said processor to reduce redundant execution ofsubstantially similar first principles simulations.
 36. The system ofclaim 34, wherein said network of interconnected resources is configuredto distribute model changes to said processor to reduce redundantrefinements of first principles simulations.
 37. The system of claim 32,further comprising remote resources connected to said processor via awide area network and configured to facilitate the semiconductor processperformed by the semiconductor processing tool.
 38. The system of claim37, wherein said remote resources comprise at least one of acomputational and a storage resource.
 39. At least one of non-volatilemedia and volatile media containing program instructions for executionon a processor, which when executed by the computer system, cause theprocessor to perform the steps of: inputting a first principles physicalmodel including a set of computer-encoded differential equations, thefirst principles physical model describing at least one of a basicphysical or chemical attribute of the semiconductor processing tool andincluding 1) a spatially resolved model of a physical geometry of thesemiconductor processing tool and 2) a grid set addressing thesemiconductor processing tool or a geometry of the semiconductorprocessing tool; inputting process data related to an actual processbeing performed by the semiconductor processing tool; setting boundaryconditions for the spatially resolved model of a physical geometry ofthe semiconductor processing tool based on said process data related tothe actual process being performed by the semiconductor processing tool;storing in a fab-level library known simulation results obtained fromsimulation modules in a device manufacturing fab and distributing theknown simulation results to other semiconductor processing tools in thedevice manufacturing fab; solving the computer-encoded differentialequations of the first principles physical model for the spatiallyresolved model concurrently with the actual process being performed andin a time frame shorter in time than the actual process being performedby: using code parallelization techniques on multiple simulation modulesin the device manufacturing fab, and re-using known simulation solutionsas initial conditions for the first principles simulation, whereinre-using known simulation solutions comprises searching in the fab-levellibrary for a closest fitting solution which if used for the initialcondition would reduce the number of iterations required by thesimulation module; providing from the solution of the computer-encodeddifferential equations solved concurrently with the actual process beingperformed a first principles simulation result; and using the simulationresult as part of a data set that characterizes the actual process beingperformed by the semiconductor processing tool.